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Advancing Patient Matching to Promote Interoperability
Session 18, February 12, 2019
Rita Torkzadeh, The Pew Charitable Trusts, @ritorkzadeh
Ben Moscovitch, The Pew Charitable Trusts, @benmoscovitch
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Rita Torkzadeh, MS
Ben Moscovitch, MA
Have no real or apparent conflicts of interest to report.
Conflict of Interest
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• Current state of patient matching
• Findings from Pew’s research
– Unique identifiers & biometrics
– Patient-involvement
– Demographic data standardization
– Referential matching
• Short and long-term recommendations
• Facilitated discussion
Agenda
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• Differentiate between the various ways hospitals currently match
patients and why the current state is insufficient
• Describe a shared vision for a coordinated nationwide strategy to
advance matching based on recently published work from multiple
groups and perspectives
• Assess research findings from focus groups with patients,
interviews with providers, and others on alternatives to improve
matching that include the use of unique identifiers, biometrics,
patient involvement, data standardization and using third party
data
• Identify recommendations based on research findings for
advancing matching in the near-term and long-term to make
nationwide progress
Learning Objectives
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Matching is not identification.
Identification is:
Matching—what is it
This is Dave Robbins.
I know because of information
he gave me: his driver’s
license or a palm scan, etc.
Matching is finding the right record.
Hospital
A
Hospital B
Dave’s
EHR
Dave’s
EHR
Dave’s
EHR
Dave’s
EHR
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Matching: Current Implications
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Matching: Implications
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Matching: Common Problems
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Denver
Nashville
Houston
Richmond
Philadelphi
a
• Literature reviews
• Conversations with experts
• Contracted research
• Focus groups with patients
• Interviews with health executives
Research Conducted
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• Generally unaware; some personal experience
• Strong support for improvements
– timely access to health data
– quicker/comprehensive care
– Avoiding unnecessary procedures and costs
• Modest differences among groups
Focus Group Patient Views
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• Internal versus external matching
• Match goal > 99 percent
• Investments: 0.5-9 FTEs
• Diminishing returns
Interviewed Health Exec Views
OK
Needs work
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• Prohibition on HHS finalizing one
• May be more advanced solutions
• Patients: preference for unique IDs
– Biometrics preferred
• Providers:
– Biometrics could help in emergencies
• Technical and costs barriers:
– Biometric template challenges/privacy
– Concerns around implementation, adoption, and expense
Opportunity 1: Unique IDs
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• Commissioned RAND report
1. Phone number verification
2. Smartphone app
3. Additional features
• Virtual clipboard
• Built into PHRs
• Mixed views from patient and providers
– Patients liked reduced paperwork, questioned widespread adoption
– Providers: benefit for patient engagement, adoption/security concerns
Opportunity 2: Patient-led
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• Been recommended for years
– Tested it with Indiana University
– Focus groups supported uniform data capture across providers
– Providers interviewed unanimously agreed this is essential
Opportunity 3: Standardization
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• Use of third-party data sources
• Among the highest reported match rates
• Some limitations & concerns among focus groups and providers
Opportunity 4: Referential
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1. Congress clarify funding
2. Verification of data
3. Assess privacy
4. Examine referential matching
5. Agreement on data standards & elements
– Standards (e.g. for address)
– Exchange collected data (e.g. email)
Near-term opportunities
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1. Establish trusted entity
– Recognized Coordinating Entity/TEFCA
2. Leverage smartphones
3. Identify infrastructure
– Standards and infrastructure that includes biometrics for
inter-organization matching
– Privacy and security
Long-term opportunities
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• Progress on inter-organization matching is essential
– Patients want and expect it
– Providers need it
• Standards are our lodestar
– Demographic data
– Future technology—biometrics
Conclusions
Quality
Costs
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1. What experience with record matching failures (or
successes) do you have? How do you measure
match rates?
2. Which solutions do you think have the most
promise, and why? What could be done to move
them forward?
3. Standardization of data showed clear benefits.
What do organizations today need to standardize
data?
4. Biometrics is part of many industries’ identity
solutions. What are your perspectives on the role
that biometrics can and should play in healthcare?
What features would need to exist for adoption
and to ensure privacy?
Discussion
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Ben Moscovitch
Project Director, Health Information Technology
The Pew Charitable Trusts
e: bmoscovitch@pewtrusts.org
@benmoscovitch
Rita Torkzadeh
Officer, Health Information Technology
The Pew Charitable Trusts
e: rtorkzadeh@pewtrusts.org
@ritorkzadeh
Questions